Apparatus and methods for distance estimation using multiple image sensors

ABSTRACT

Data streams from multiple image sensors may be combined in order to form, for example, an interleaved video stream, which can be used to determine distance to an object. The video stream may be encoded using a motion estimation encoder. Output of the video encoder may be processed (e.g., parsed) in order to extract motion information present in the encoded video. The motion information may be utilized in order to determine a depth of visual scene, such as by using binocular disparity between two or more images by an adaptive controller in order to detect one or more objects salient to a given task. In one variant, depth information is utilized during control and operation of mobile robotic devices.

CROSS-REFERENCE AND RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/948,885 filed Apr. 9, 2018, which is related to co-pending andco-owned U.S. patent application Ser. No. 14/285,385, entitled“APPARATUS AND METHODS FOR REAL TIME ESTIMATION OF DIFFERENTIAL MOTIONIN LIVE VIDEO”, filed on May 22, 2014, and co-owned U.S. patentapplication Ser. No. 14/285,466, entitled “APPARATUS AND METHODS FORROBOTIC OPERATION USING VIDEO IMAGERY”, filed on May 22, 2014, now U.S.Pat. No. 9,713,982, each of the foregoing incorporated herein byreference in its entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND Field of the Disclosure

The present disclosure relates to, inter alia, computerized apparatusand methods for processing imagery from multiple sources.

Description of Related Art

Object recognition in the context of computer vision relates to findinga given object in an image or a sequence of frames in a video segment.Typically, video frames may contain multiple objects moving in one ormore directions on a still or moving background. Object representations,also referred to as the “view”, may change from frame to frame due to avariety of object transformations, such as rotation, movement,translation, change in lighting, background, noise, appearance of otherobjects, partial blocking and/or unblocking of the object, and/or otherobject transformations. Robotic devices often employ video fornavigation, target selection and/or obstacle avoidance. Determiningmotion of object from a moving robotic platform may requireimplementation of differential motion detection in an energy efficientmanner. Depth of visual scene (e.g., distance to one or more objects)may be useful for operation of mobile robots as well.

SUMMARY

One aspect of the disclosure relates to a non-transitorycomputer-readable storage medium having instructions embodied thereon,the instructions being executable to perform a method of determining adistance to an object.

In another aspect, a method of determining distance to an object isdisclosed. In one implementation, the object is disposed within a visualscene, and the method includes: producing a video stream by interleavingimages of a first plurality of images and a second plurality of imagesof the visual scene; and evaluating the video stream to determine thedistance. In one variant, individual images of the first and secondpluralities of images are provided by first and second cameras,respectively, the second camera being separated spatially from the firstcamera.

In another variant, the evaluation comprises determination of abinocular disparity between at least a portion of the second and thefirst pluralities of images, the disparity being related to the distanceand the spatial separation; and the distance determination is based onthe disparity determination.

In a further variant, the evaluation further comprises encoding thevideo stream using an encoder process comprising motion estimation, themotion estimation configured to provide information related to adisplacement of a first representation of the object within a givenimage of the video stream relative a second representation of the objectwithin a preceding image of the video stream. Individual ones of thefirst and second pluralities of images comprise for instance a pluralityof pixels, and the encoder process includes one of e.g., MPEG-4, H.262,H.263, H.264, and H.265 encoders.

In another aspect, a non-transitory computer-readable storage mediumhaving instructions embodied thereon is disclosed. In oneimplementation, the instructions are executable to produce a combinedimage stream from first and second sequences of images of a sensoryscene by at least: selecting a first image and a second image from thesecond sequence to follow a first image from the first sequence, thesecond image from the second sequence following the first image from thesecond sequence; selecting second and third images from the firstsequence to follow the second image from the second sequence, the thirdimage from the first sequence following the second from the firstsequence; and evaluating the combined image stream to determine a depthparameter of the scene.

In one variant, the first and the second image sequences are provided bya first and a second image sensor, respectively, the first image sensorbeing disposed spatially separated from the second image sensor. Thefirst image sensor and the second image sensor are configured to provideimages of the sensory scene, and the spatial separation is configured toproduce a binocular disparity for the first image from the secondsequence relative the first image from the first sequence; and thesecond image from the second sequence relative the second image from thefirst sequence. The depth is determined based on the disparity.

In another variant, individual images of the first image sequence andthe second image sequence comprise a plurality of pixels; and theevaluating comprises encoding the combined stream using a motionestimation encoder.

In yet another variant, the combined image stream comprises the firstimage from the first sequence, followed by the first image from thesecond sequence followed by the second image from the second sequencefollowed by the second image from the first sequence followed by thethird image from the first sequence. The motion estimation encoder isconfigured to, in one particular implementation: determine a firstversion of the disparity based on encoding the first image from thefirst sequence and the first image from the second sequence; determine asecond version of the disparity based on encoding the second image fromthe second sequence and the second image from the first sequence, thesecond version of the disparity having an opposite sign relative thefirst version; determine a first motion of the second image sequencebased on encoding the first image from the second sequence followed bythe second image from the second sequence; and determine a second motionof the first image sequence based on encoding the second image from thefirst sequence followed by the third image from the first sequence.

In a further aspect, an image processing apparatus is disclosed. In oneimplementation, the apparatus includes: an input interface configured toreceive a stereo image of a visual scene, the stereo image comprising afirst frame and a second frame; a logic component configured to form asequence of frames by arranging the first and the second framessequentially with one another within the sequence; a video encodercomponent in data communication with the logic component and configuredto encode the sequence of frames to produce a sequence of compressedframes; and a processing component in data communication with the videoencoder and configured to obtain motion information based on anevaluation of the compressed frames.

In one variant, the sequence of compressed frames comprises a key framecharacterized by absence of the motion information, and the processingcomponent is configured to not utilize the key frame during theevaluation of the compressed frames.

In another variant, the processing component is further configured todetermine, based on the motion information, a depth parameter associatedwith the visual scene. The input interface is further configured toreceive another image comprising a third frame, the first, second andthird frames being provided respectively by first, second, and thirdcameras disposed spatially separately from one another. The sequence offrames is formed by arranging the third frame sequentially with thefirst frame and the second frame.

These and other objects, features, and characteristics of the systemand/or method disclosed herein, as well as the methods of operation andfunctions of the related elements of structure and the combination ofparts and economies of manufacture, will become more apparent uponconsideration of the following description and the appended claims withreference to the accompanying drawings, all of which form a part of thisspecification, wherein like reference numerals designate correspondingparts in the various figures. It is to be expressly understood, however,that the drawings are for the purpose of illustration and descriptiononly and are not intended as a definition of the limits of thedisclosure. As used in the specification and in the claims, the singularform of “a”, “an”, and “the” include plural referents unless the contextclearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a graphical illustration depicting a top view of roboticapparatus configured to acquire stereo imagery, in accordance with oneor more implementations.

FIG. 1B is a graphical illustration depicting a side view of a roboticapparatus comprising an adaptive controller apparatus of the disclosure,configured for autonomous navigation, in accordance with one or moreimplementations.

FIG. 2A is a graphical illustration depicting stereo imagery inputobtained with two spatially displaced cameras for use with the disparitydetermination methodology, according to one or more implementations.

FIG. 2B is a graphical illustration depicting disparity betweenrepresentations of objects corresponding to the frames of stereo imageryshown in FIG. 2A, according to one or more implementations.

FIG. 2C is a graphical illustration depicting input frames comprising aplurality of moving objects for use with the motion extraction,according to one or more implementations.

FIG. 3A is a logical block diagram depicting a determination of an inputstream for motion processing using an alternating interleaver of stereoimagery input, according to one or more implementations.

FIG. 3B is a logical block diagram depicting a determination of an inputstream for motion processing using an alternating interleaver of stereoimagery input, according to one or more implementations.

FIG. 4A is a functional block diagram depicting a processing apparatusconfigured to determine disparity from a dual image source, according toone or more implementations.

FIG. 4B is a functional block diagram depicting a processing apparatusconfigured to determine disparity from multiple image sources, accordingto one or more implementations.

FIG. 5A is a graphical illustration depicting triple cameraconfiguration used for disparity determination using image interleaving,according to one or more implementations.

FIG. 5B is a graphical illustration depicting quad camera configurationused for disparity determination using image interleaving, according toone or more implementations.

FIG. 5C is a graphical illustration depicting linear multiple cameraconfiguration useful for determining multiple depths scales using imageinterleaving, according to one or more implementations.

FIG. 6A is a graphical illustration depicting an alternatinginterleaving of triple image input for use with the motion extraction,according to one or more implementations.

FIG. 6B is a graphical illustration depicting an alternatinginterleaving of quad image input for use with the motion extraction,according to one or more implementations.

FIG. 7 is a functional block diagram depicting a motion extractionapparatus, according to one or more implementations.

FIG. 8 is a functional block diagram depicting a video processingsystem, comprising a differential motion extraction apparatus, accordingto one or more implementations.

FIG. 9A is a graphical illustration depicting an encoded object for usewith the motion extraction, according to one or more implementations.

FIG. 9B is a graphical illustration depicting motion of an encodedobject for use with the motion extraction methodology, according to oneor more implementations.

FIG. 9C is a graphical illustration depicting spatial distribution ofmotion extracted from encoded video, according to one or moreimplementations.

FIG. 10 is a logical flow diagram illustrating a method of determining asalient feature using encoded video motion information, in accordancewith one or more implementations.

FIG. 11 is a logical flow diagram illustrating a method of dataprocessing useful for determining features, in accordance with one ormore implementations.

FIG. 12 is a logical flow diagram illustrating a method of executing anaction configured based on a gesture detected using motion information,in accordance with one or more implementations.

FIG. 13 is a logical flow diagram illustrating a method of determining adepth of visual scene using encoded interleaved stereo imageinformation, in accordance with one or more implementations.

FIG. 14 is a logical flow diagram illustrating a method of determiningdistance to objects using motion of interleaved image stream, inaccordance with one or more implementations.

FIG. 15 is a logical flow diagram illustrating a method of executing anaction configured based on detecting an object in motion information, inaccordance with one or more implementations.

FIGS. 16A-16D illustrate gestures of a human operator used forcommunicating control indications to a robotic device (such as onecomprising a distance determination apparatus as described herein), inaccordance with one or more implementations.

FIG. 17 is a graphical illustration depicting an exemplary unmannedrobotic apparatus comprising distance determination apparatus of thedisclosure configured for autonomous navigation, in accordance with oneor more implementations.

All Figures disclosed herein are © Copyright 2014 Brain Corporation. Allrights reserved.

DETAILED DESCRIPTION

Implementations of the present disclosure will now be described indetail with reference to the drawings, which are provided asillustrative examples so as to enable those skilled in the art topractice the present technology. Notably, the figures and examples beloware not meant to limit the scope of the present disclosure to a singleimplementation, but other implementations are possible by way ofinterchange of or combination with some or all of the described orillustrated elements. Wherever convenient, the same reference numberswill be used throughout the drawings to refer to same or like parts.

Although the system(s) and/or method(s) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation may be combined with one or morefeatures of any other implementation

In the present disclosure, an implementation showing a singularcomponent should not be considered limiting; rather, the disclosure isintended to encompass other implementations including a plurality of thesame component, and vice-versa, unless explicitly stated otherwiseherein.

Further, the present disclosure encompasses present and future knownequivalents to the components referred to herein by way of illustration.

As used herein, the term “bus” is meant generally to denote all types ofinterconnection or communication architecture that is used to access thesynaptic and neuron memory. The “bus” could be optical, wireless,infrared or another type of communication medium. The exact topology ofthe bus could be for example standard “bus”, hierarchical bus,network-on-chip, address-event-representation (AER) connection, or othertype of communication topology used for accessing, e.g., differentmemories in pulse-based system.

As used herein, the terms “computer”, “computing device”, and“computerized device”, include, but are not limited to, personalcomputers (PCs) and minicomputers, whether desktop, laptop, orotherwise, mainframe computers, workstations, servers, personal digitalassistants (PDAs), handheld computers, embedded computers, programmablelogic device, personal communicators, tablet or “phablet” computers,portable navigation aids, J2ME equipped devices, smart TVs, cellulartelephones, smart phones, personal integrated communication orentertainment devices, or literally any other device capable ofexecuting a set of instructions and processing an incoming data signal.

As used herein, the term “computer program” or “software” is meant toinclude any sequence or human or machine cognizable steps which performa function. Such program may be rendered in virtually any programminglanguage or environment including, for example, C/C++, C#, Fortran,COBOL, MATLAB™, PASCAL, Python, assembly language, markup languages(e.g., HTML, SGML, XML, VoXML), and the like, as well as object-orientedenvironments such as the Common Object Request Broker Architecture(CORBA), Java™ (including J2ME, Java Beans), Binary Runtime Environment(e.g., BREW), and other languages.

As used herein, the terms “connection”, “link”, “synaptic channel”,“transmission channel”, “delay line”, are meant generally to denote acausal link between any two or more entities (whether physical orlogical/virtual), which enables information exchange between theentities.

As used herein the term feature may refer to a representation of anobject edge, determined by change in color, luminance, brightness,transparency, texture, and/or curvature. The object features maycomprise, inter alia, individual edges, intersections of edges (such ascorners), orifices, and/or curvature

As used herein, the term “memory” includes any type of integratedcircuit or other storage device adapted for storing digital dataincluding, without limitation, ROM. PROM, EEPROM, DRAM, Mobile DRAM,SDRAM, DDR/2 SDRAM, EDO/FPMS, RLDRAM, SRAM, “flash” memory (e.g.,NAND/NOR), memristor memory, and PSRAM.

As used herein, the terms “processor”, “microprocessor” and “digitalprocessor” are meant generally to include all types of digitalprocessing devices including, without limitation, digital signalprocessors (DSPs), reduced instruction set computers (RISC),general-purpose (CISC) processors, microprocessors, gate arrays (e.g.,field programmable gate arrays (FPGAs)), PLDs, reconfigurable computerfabrics (RCFs), array processors, secure microprocessors, andapplication-specific integrated circuits (ASICs). Such digitalprocessors may be contained on a single unitary IC die, or distributedacross multiple components.

As used herein, the term “network interface” refers to any signal, data,or software interface with a component, network or process including,without limitation, those of the FireWire (e.g., FW400, FW800, and/orother FireWire implementation), USB (e.g., USB2), Ethernet (e.g.,10/100, 10/100/1000 (Gigabit Ethernet), 10-Gig-E, etc.), MoCA, Coaxsys(e.g., TVnet™), radio frequency tuner (e.g., in-band or OOB, cablemodem, etc.), Wi-Fi (802.11), WiMAX (802.16), PAN (e.g., 802.15),cellular (e.g., 3G, LTE/LTE-A/TD-LTE, GSM, and/or other cellularinterface implementation) or IrDA families.

As used herein, the terms “pulse”, “spike”, “burst of spikes”, and“pulse train” are meant generally to refer to, without limitation, anytype of a pulsed signal, e.g., a rapid change in some characteristic ofa signal, e.g., amplitude, intensity, phase or frequency, from abaseline value to a higher or lower value, followed by a rapid return tothe baseline value and may refer to any of a single spike, a burst ofspikes, an electronic pulse, a pulse in voltage, a pulse in electricalcurrent, a software representation of a pulse and/or burst of pulses, asoftware message representing a discrete pulsed event, and any otherpulse or pulse type associated with a discrete information transmissionsystem or mechanism.

As used herein, the term “receptive field” is used to describe sets ofweighted inputs from filtered input elements, where the weights may beadjusted.

As used herein, the term “Wi-Fi” refers to, without limitation, any ofthe variants of IEEE-Std. 802.11 or related standards including 802.11a/b/g/n/s/v and 802.11-2012.

As used herein, the term “wireless” means any wireless signal, data,communication, or other interface including without limitation Wi-Fi,Bluetooth, 3G (3GPP/3GPP2), HSDPA/HSUPA, TDMA, CDMA (e.g., IS-95A,WCDMA, and/or other wireless interface implementation), FHSS, DSSS, GSM,PAN/802.15, WiMAX (802.16), 802.20, narrowband/FDMA, OFDM, PCS/DCS,LTE/LTE-A/TD-LTE, analog cellular, CDPD, RFID or NFC (e.g., EPC GlobalGen. 2, ISO 14443, ISO 18000-3), satellite systems, millimeter wave ormicrowave systems, acoustic, and infrared (e.g., IrDA).

The present disclosure provides, among other things, apparatus andmethods for determining depth of field of a scene based on processinginformation from multiple sources detecting motion of objects and/orfeatures in video in real time. The video information may comprise forexample multiple streams of frames received from a plurality of camerasdisposed separate from one another. Individual cameras may comprise animage sensor (e.g., charge-coupled device (CCD), CMOS device, and/or anactive-pixel sensor (APS), photodiode arrays, and/or other sensors). Inone or more implementations, the stream of frames may comprise a pixelstream downloaded from a file. An example of such a file may include astream of two-dimensional matrices of red green blue RGB values (e.g.,refreshed at a 25 Hz or other suitable frame rate). It will beappreciated by those skilled in the art when given this disclosure thatthe above-referenced image parameters are merely exemplary, and manyother image representations (e.g., bitmap, luminance-chrominance (YUV,YCbCr), cyan-magenta-yellow and key (CMYK), grayscale, and/or otherimage representations) are equally applicable to and useful with thevarious aspects of the present disclosure. Furthermore, data framescorresponding to other (non-visual) signal modalities such as sonograms,infrared (IR), radar or tomography images may be equally compatible withthe processing methodology of the disclosure, or yet otherconfigurations.

The video processing methodology described herein may enable a roboticcontroller to obtain motion and/or distance information using aspecialized hardware video encoder. Use of dedicated video encodersprovides a computationally efficient way to determine motion and/ordistance using video signals compared to processing techniques thatemploy general purpose processors for performing computations (e.g.,optical flow, block matching, phase correlations and/or other.Computational efficiency of hardware video encoders may be leveraged topreduce energy use, complexity, size, and/or cost of the processingcomponent, increase autonomy of robotic device using the computationallyefficient controller, and/or increase processing performance (e.g.,image resolution, frame rate, number of cameras) for a given hardwarespecifications compared to the prior art.

Processing data from multiple spatially distributed sources may enabledepth of field determination using a disparity methodology. In someimplementations of stereo vision, distance d to an object may bedetermined using binocular disparity D as follows:

$\begin{matrix}{d \propto \frac{1}{D}} & \left( {{Eqn}.\mspace{14mu} 1} \right)\end{matrix}$

FIG. 1A depicts a top view of mobile robotic apparatus comprising twocameras configured to provide sensory information for determiningdistance based on the disparity. The apparatus 100 may comprise forinstance a robotic vehicle outfitted with a motion detection apparatusconfigured in accordance with one or more implementations, e.g., such asillustrated in FIGS. 4A-4B, below. The robotic apparatus 100 maycomprise left and right cameras 106, 108 disposed at a distance 102 fromone another. The robotic apparatus 100 may navigate in a direction 104.One or more obstacles may be present in path of the apparatus 100, e.g.,a ball 112 and a box 122, disposed at distance 110, 120, respectively,from the apparatus 110. Due to the spatial separation 102 between thecameras 106, 108, travel paths from a given object (e.g., 114, 116 forthe ball 112), 124, 126 for the box 122) may be unequal to one another.As shown in FIG. 1A, the path 114 is longer compared to the path 116,and the path 126 is longer than the path 124.

Difference in path lengths may cause a difference in apparent positionof the objects 112, 122 in image frame(s) provided by the camera 106relative the image frame provided by the camera 108.

FIG. 2A depicts a typical stereo imagery input for use with thedisparity determination methodology, according to one or moreimplementations. The frames 200, 210 in FIG. 2A may be acquired by thetwo spatially displaced cameras 106, 108 of the apparatus 100 in FIG.1A. Object representations 216, 212 of the frame 210 may be displacedhorizontally relative to object representations 206, 202, respectively,of the frame 200. Object representations 202, 212, 206, 216 maycorrespond to objects 112, 122, respectively, in FIG. 1A.

FIG. 2B illustrates the disparity between representations of objectscorresponding to the frames of stereo imagery shown in FIG. 2A. Objectrepresentations 236, 246 in frame 23 may be characterized by ahorizontal disparity 234, and similarly object representations 222 232may be characterized by a corresponding horizontal disparity 224.Disparity 224, 234 may be inversely proportional to distance between thecamera and the respective object (e.g., the distance 110, 120 in FIG.1A); i.e., the shorter the distance, the greater the disparity, due tothe greater subtended arc.

FIG. 1B depicts a mobile robotic apparatus comprising a motion detectionapparatus configured, e.g., in accordance with the exemplaryimplementations illustrated in FIGS. 7-8, infra. The robotic apparatus160 may comprise a camera 166. The camera 166 may be characterized by afield of view 168 (e.g., an extent of the observable world that may becaptured by the camera lens at a given moment). The camera 166 mayprovide information associated with objects within the field of view168. In some implementations, the camera 166 may provide frames ofpixels of luminance and/or color, refreshed at 25 Hz frame rate.However, it will be appreciated that, in some implementations, otherframe rates may be used (whether constant or variable), as may othertypes of information provided by the camera(s) 166.

One or more objects (e.g., a floor 170, a stationary object 176, amoving object (e.g., ball 174), and/or other objects) may be present inthe camera field of view. The motion of the objects may result in adisplacement of pixels representing the objects within successiveframes, such as is described in U.S. patent application Ser. No.13/689,717 filed on Nov. 30, 2012 and entitled “APPARATUS AND METHODSFOR OBJECT DETECTION VIA OPTICAL FLOW CANCELLATION”, incorporated,herein by reference in its entirety.

When the robotic apparatus 160 is in motion, such as shown by arrow 164in FIG. 1B, motion of the objects within the camera 166 field if view168 (e.g., denoted by arrows 172, 178, 180 in FIG. 1B) may comprise theself-motion component and the object motion component. By way of anon-limiting example, motion of objects in FIG. 1B may comprise apparentmotion 180 of the stationary background 176 and the boundary (e.g., thecomponent 172 associated with the floor boundary); (ii) component 178associated with the moving ball 174 that comprises a superposition ofthe ball displacement and motion of the camera; and/or other components.As noted previously, determination of the ball 174 motion may beparticularly challenging when the camera 160 is in motion (e.g., duringpanning) and/or when the field of view is changing (e.g., when zoomingin/out).

FIG. 2C depicts two exemplary frames (e.g., provided by the camera 166in FIG. 1A) comprising multiple moving objects useful with the motionestimation methodology described herein. The frames 240, 250 maycomprise an object 246, 256 that may move in a given direction (e.g.,288). The frames 240, 250 may comprise an object 242, 252 that may moveback and forth in a direction indicated by arrow 244. Motion alongcurved trajectories may be resolved by using linear piece-wiseapproximation, wherein motion between successive frames may beinterpreted as linear. An increased frame rate and/or image resolutionmay be employed with complex motion trajectories. In someimplementations of target approach by a robotic device, the object 242may comprise a target (e.g., ball) that may be moved back and forth inorder to indicate to a controller of, e.g., the robotic vehicle 160 inFIG. 1B, a target to follow. Frames 240, 250 may represent position ofobjects at two time instances. Due to the presence of multiple motions,detection of object 242, 252 may be not straightforward due to, forexample, portions of the frames 250 being characterized by differentialmotion.

In some implementations of object detection in the presence ofdifferential motion, background (and/or self-motion) may be determinedusing a statistical analysis of motion distribution within a givenencoded frame. Various statistical parameters may be determined, e.g.,median, mean plus/minus n standard deviations, and/or others, in orderto determine one or more prevailing (dominant) motion vectors for theframe. The prevailing motion may be removed (e.g., via a vectorsubtraction) from the frame motion distribution in order to determineresidual motion. The residual motion may be analyzed (e.g., using athreshold technique) in order to detect one or more features that may bemoving differently from the prevailing motion.

In one or more implementations of object detection in the presence ofdifferential motion, prevailing motion may be determined using aclustering technique. For example, a motion filed within the frame maybe partitioned into a plurality of clusters based on analysis of motiondistribution. The largest area cluster may be associated with theprevailing (dominant) motion, or may be removed (masked off) from theframe to obtain residual motion distribution. The residual motion may beanalyzed in order to determine the presence of one or more featuresbased on remaining clusters of motion.

In some implementations, image frames provided by a plurality of cameras(e.g., the cameras 106, 108 in FIG. 1A) may be utilized in order todetermine depth of field and/or distance to objects using the disparityinformation. Comparing object representations 206, 202 of frame 200 toobject representations 216, 212 of frame 210, the disparity may beconsidered as object motion occurring between the capture of frame 200and the capture of frame 210 in FIG. 2A. The disparity (e.g., apparentmotion) 224, 234 in FIG. 32B may be obtained using, in the exemplaryimplementation, motion estimation. Various motion estimation algorithmsexist (e.g., optical flow methodology, such as that described in U.S.patent application Ser. No. 13/689,717 filed on Nov. 30, 2012 andentitled “APPARATUS AND METHODS FOR OBJECT DETECTION VIA OPTICAL FLOWCANCELLATION”, incorporated herein by reference in its entirety, each ofwhich may be used consistent with the various aspects of the presentdisclosure.

In some implementations, the apparent motion due to disparity may bedetermined using motion estimation information provided by a videoencoder. In order to enable motion estimation by an encoder, framesprovided by individual cameras (e.g., 106, 108 in FIG. 1A) may becombined to form a common video stream. FIG. 3A illustratesdetermination of an input stream for motion processing using analternating interleaver of stereo frame input, according to one or moreimplementations. In FIG. 3A, the frame sequences 300,310 (also referredto as channel A, B) may correspond to data provided by two cameras(e.g., left/right cameras 106, 108 in FIG. 1A), and/or data loaded froma disc or other source, in one or more implementations. The framesequences 300, 310 comprising (e.g., frames 302, 312) may be processedby an alternating interleaver process 320 configured to produce aninterleaved frame sequence 322. The sequence 322 may comprisealternating frames (e.g., 302, 312) from left/right cameras, in someimplementations. In some implementations, the frames from left/rightcameras (e.g., 302, 312) may be acquired simultaneous with one anotherusing, e.g., multiple camera synchronization.

FIG. 3B illustrates determination of an input stream for motionprocessing using an alternating interleaver of stereo frame input,according to one or more implementations. The A, B frame sequences 300,310 comprising (e.g., frames 302, 312) may be processed by thealternating interleaver process 340 configured to produce an interleavedframe sequence 342. The sequence 342 may be configured to comprisealternating pairs of frames from a given channel. As shown in FIG. 3A,frames B1, B2 from channel B acquired at times t1, t2, may be followedby frames A1, A2 from channel A acquired at times t1, t2, followed byframes B3, B4 from channel B acquired at times t3, t4, whereint4>t3>t2>t1. In some implementations (not shown), the frame A1 may berepeated and/or preceded by a blank frame in the interleaved sequence342. Use of an alternating approach may provide, inter alia, both motionand disparity information within a single encoded stream.

Streams of interleaved frames (e.g., 322, 342 FIGS. 3A-3B, and/or shownin FIGS. 6A-6B, below) may be utilized in order to determine depth offield of view and/or distance to objects using motion encoding, asdescribed in detail below with respect to FIGS. 4A-5.

FIG. 4A illustrates a processing apparatus configured to determinedisparity from two image sources, according to one or moreimplementations. The apparatus 400 may comprise two image sources 404,405 configured to provide information environment 402. In someimplementations of visual data processing, the sources 404, 405 maycomprise digital and/or analog cameras disposed separate from oneanother. Individual cameras may comprise an image sensor (CCD, CMOSdevice, and/or an APS, photodiode arrays, and/or other sensors). It willbe appreciated that in some implementations, such separation between theimage sensors may be achieved even when the sensors are disposed on thesame substrate or “chip” (e.g., two sensors placed at opposite ends ofthe same substrate/chip). In one or more implementations, the imagesources 4054, 405 may comprise video files on a storage device. Anexample of such a file may include a stream of two-dimensional matricesof red green blue RGB values (e.g., refreshed at a 25 Hz or othersuitable frame rate). It will be appreciated by those skilled in the artwhen given this disclosure that the above-referenced image parametersare merely exemplary, and many other image representations (e.g.,bitmap, luminance-chrominance (YUV, YCbCr), cyan-magenta-yellow and key(CMYK), grayscale, and/or other image representations) are equallyapplicable to and useful with the various aspects of the presentdisclosure. Furthermore, data frames corresponding to other (non-visual)signal modalities such as sonograms, IR, radar, or tomography images maybe equally compatible with the processing methodology of the disclosure,or yet other configurations.

Image frames 406, 407 provided by the sources 404, 405 may beinterleaved by the interleaver apparatus 410. In some implementations,the interleaver apparatus 410 may comprise 2×1 multiplexer configured toprovide one of the input channels 406, 407 at its output 412 at a giventime. The output 412 may comprise an alternating interleaved stream(e.g., 322), an alternating interleaved stream of frames (e.g., 342 inFIG. 3B), or yet other option, in accordance with one or moreimplementations.

The interleaved output 412 may be provided to a motion estimationcomponent 420. In one or more implementations, the motion estimationcomponent may comprise a video encoder comprising one or more motionestimation operations. The component 420 may comprise for instance adedicated integrated circuit (IC) disposed on a single or multiple die),a component of a processing system (e.g., video encoder block of aSnapdragon® system on a chip), an ASIC, an FPGA with a video encoderintellectual property (IP) core, an OEM printed circuit board, and/orother. Video encoding effectuated by the component 420 may comprise anyapplicable standard comprising motion estimation between one or morecurrent images and one or more preceding images. Some exemplary encodingimplementations include H.264/MPEG-4 advanced video coding described,e.g., in ISO/IEC 14496-10, 2009—MPEG-4 Part 10, Advanced Video Coding,H.263 standard described in, e.g., ITU-T H.263 TELECOMMUNICATIONSTANDARDIZATION SECTOR OF ITU (01/2005) SERIES H: AUDIOVISUAL ANDMULTIMEDIA SYSTEMS Infrastructure of audiovisual services—Coding ofmoving video, Video coding for low bit rate communication; H.262/MPEG-2,described in e.g., ISO/IEC 13818-2 2013-10-01 Informationtechnology—Generic coding of moving pictures and associated audioinformation—Part 2: Video, H.265 standard described in, e.g., ITU-TH.263 TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (04/2013), SERIESH: AUDIOVISUAL AND MULTIMEDIA SYSTEMS Infrastructure of audiovisualservices—Coding of moving video, High efficiency video coding; each ofthe foregoing being incorporated herein by reference in its entirety.See also Exhibit I hereto, which contains exemplary computer code usefulfor processing image data consistent with, e.g., the ISO/IEC 1196-10 andH.265 Standards referenced above.

In some implementations, the motion estimation component 420 maycomprise logic configured to determine motion using optical flow, and/orother motion estimation algorithms such as but not limited to:block-matching algorithm, phase correlation, as well as determininglocations of one or more features and estimating the motion ofindividual detected features.

Output 422 of the motion estimation component may be provided to aprocessing component 430 configured to determine one or more parametersof interest, including e.g., depth of the scene 402 and/or distance toobjects that may be present in the scene, using motion based disparitydetermination methodology.

Returning now to FIG. 3A, the encoding of the interleaved frame sequence322 by the motion estimation component 420 of FIG. 4A is depicted byarrow 324. In some implementations, the component 420 may compriseMPEG-4/H.264 encoder configured to produce the encoded stream 330. Theencoding of frame pair 302, 312 may produce motion information for theencoded frame 334. The motion information of the frame 334 may comprisee.g., horizontal and/or vertical displacement (dx, dy) of blocks ofpixels (macroblocks) and be interpreted as caused by a disparity betweenscene representations of frame 302 and frame 304. In someimplementations of stereo vision (e.g., as described above with respectto FIG. 1A), analysis of motion information for the frame 334 (performedby the component 430 in FIG. 4A) may produce the disparity D betweenleft and right image frames. As shown in FIG. 3A, the encoded frames334, 338 may provide disparity estimates D1, D2 associated with framesacquired at times t1, t2. Frames 332, 336 may be ignored (skipped) forthe purposes of the disparity determination. Disparity estimates D1, D2may be used to determine distance to one or more objects within theframes 330.

In one or more implementations, the component 430 may be configured toparse the compressed video stream 422 in order to obtain motioninformation (e.g., map of vectors 916 in FIG. 9A). By way of anillustration, the motion information may comprise a macroblock locationL (e.g., index), x-component, and y-component of motion of pixelsassociated with the macroblock location L. The extracted motioninformation may be used for disparity and or distance determination.Output 432 of the component 430 may be provided to another component(e.g., a controller of a robot). Various uses of the depth informationare contemplated such as, for example, object detection, objectlocalization, distance estimation, trajectory planning, gesturedetection, and/or others that will be recognized by those of ordinaryskill when provided the present disclosure.

Returning now to FIG. 3B, encoding of the interleaved frame sequence 342by the motion estimation component 420 of FIG. 4A is depicted by arrow344 in FIG. 3B. In some implementations, the component 420 may compriseMPEG-4/H.264 encoder configured to produce encoded stream 350. Inencoding of frame pair 302, 312 may produce motion information for theencoded frame 354. The motion information of the frame 354 may comprisehorizontal and/or vertical displacement (dx, dy) of blocks of pixels andbe interpreted as caused by a disparity between scene representations offrame 302 and frame 304. In some implementations, of stereo vision(e.g., as described above with respect to FIG. 1A) analysis of motioninformation for the frame 354 (performed by the component 430 in FIG.4A) may produce the disparity D between left and right image frames. Asshown in FIG. 3B, the encoded frames 354, 362 may provide disparityestimates D1, D3 associated with frames acquired at times t1, t3. Theencoded frame 358 may provide negative disparity estimate (−D2)associated with frames acquired at time t2. Frames 356, 362 may providemotion information associated with the frame sequence 310. Frame 360 mayprovide motion information associated with the frame sequence 300.Disparity estimates D1, D2, D3 may be used to determine distance to oneor more objects within the frames 350 using, e.g., Eqn. 1.

Although interleaving of frames from two sources is illustrated in FIGS.3A-3B, the methodology described herein may be employed for anypractical number of sources (e.g., three, four as shown and describedwith respect to FIGS. 5A-5C below, and/or a greater number of sources).

FIG. 4B illustrates a processing apparatus configured to determinedisparity from two or more image sources, according to one or moreimplementations. The apparatus 440 may comprise a plurality of imagesources (e.g., 443, 445) configured to provide information related tothe environment 402. In some implementations of visual data processing,the sources 443, 445, 405 may comprise sources described with respect toFIG. 4A above.

Image frames 446, 448 provided by the sources 443, 445 may beinterleaved by the interleaver apparatus 450. In some implementations,the interleaver apparatus 450 may comprise N×1 multiplexer configured toprovide date from one of the input channels 446, 448 at its output 452at a given time. The output 452 may comprise an alternating interleavedstream (e.g., 600 in FIG. 6A), an alternating interleaved stream offrames (e.g., constructed similar to the stream 342 in FIG. 3B) inaccordance with one or more implementations.

The interleaved output 452 may be provided to a processing component460. The component 460 may comprise motion estimation logic. In one ormore implementations, the motion estimation logic may comprise a videoencoder comprising motion estimation operation. The component 460 maycomprise a dedicated integrated circuit (IC) disposed on a single ormultiple die), a component of a processing system (e.g., video encoderblock of a Snapdragon® system on a chip), an ASIC, an FPGA with a videoencoder intellectual property (IP) core, an OEM printed circuit board,and/or other. Video encoding effectuated by the component 460 maycomprise any applicable standard comprising motion estimation betweencurrent frame and preceding frame. In some implementations, the motionestimation component 460 may comprise logic configured to determinemotion using optical flow, and/or other motion estimation algorithmssuch as but not limited to: block-matching algorithm, phase correlation,as well as determining locations of features and estimating the motionof those features. In one or more implementations wherein the input 452may be encoded using a video encoder (e.g., MPEG-4, H.265), thecomponent 460 may be configured to parse the encoded video stream inorder to obtain motion information (e.g., map of vectors 916 in FIG.9A).

The component 460 may comprise logic configured to determine depth ofthe scene 402 and/or distance to objects that may be present in thescene using motion determined based disparity determination methodology.The extracted motion information may be used for disparity and ordistance determination. Output 462 of the component 460 may be providedto another component (e.g., a controller of a robot). Various uses ofthe depth information may be contemplated such as, for example, objectdetection, object localization, distance estimation, trajectoryplanning, gesture detection, and/or others. Determining motion disparityand/or the distance may be performed for example using operationsdescribed above with respect to FIGS. 3A-4A. In some implementations ofan encoder with motion estimation (e.g., MPEG-4/H.264), the encodedstream 330, and/or stream 350 in FIGS. 3A-3B and/or stream 422 in FIG.4A may comprise one or more frames (also referred to as keyframes) thatmay not contain motion information. The processing component 430 and/or460 of FIGS. 4A-4B may be configured to detect and ignore (e.g., skip)frames that do not convey motion information.

The apparatus 440 of FIG. 4B may be utilized with multi-cameraconfigurations, e.g., such as described below with respect to FIGS.5A-5C. FIG. 5A illustrates a triple-camera configuration useful withdisparity determination using image interleaving, according to one ormore implementations. The camera configuration 500 may comprise threecameras 510, 520, 530, denoted A, B, C, respectively. In one or moreimplementations, the configuration 500 may comprise a pair ofhorizontally spaced cameras (e.g., left 510, right 520) and a verticallyspaced camera (530). In some implementations (e.g., such as illustratedin FIG. 5C), the cameras 510, 520, 530 may be disposed in a lineararray, and/or another configuration. Frames provided by the cameras 510,520, 530 may be interleaved using any applicable methodologies,including these described with respect to FIGS. 3A-3B, and/or 6A herein.

Various interleaving sequences may be employed when processing framesprovided by the cameras 510, 520, 530. By way of illustration, encodinginterleaved frame stream ABCA . . . (e.g., the stream 600 shown in FIG.6A) comprising frames provided by the cameras 510, 520, 530 using amotion estimation encoder, may provide motion due to disparity shown byarrows 504, 514, 524 in FIG. 5A. Encoding interleaved frame stream ACBA. . . , (e.g., 610 in FIG. 6A) using a motion estimation encoder, mayprovide motion due to disparity shown by arrows 526, 516, 506 in FIG.5A. Various other interleaving sequences may be utilized, such as, forexample, ABBCCAABBCCAA . . . , BACA . . . and/or other.

FIG. 5B illustrates a quad-camera configuration useful with disparitydetermination using image interleaving, according to one or moreimplementations. The camera configuration 550 may comprise four cameras550, 552, 556, 558, denoted A, B, C, D, respectively. In one or moreimplementations, the configuration 550 may comprise two pairs ofhorizontally spaced cameras (e.g., left top 550, right top 552 and leftbottom 550, right bottom 556) vertically spaced from one another.Various other spatial camera configurations may be utilized as well.Frames provided by the cameras 552, 554, 556, 558 may be interleavedusing any applicable methodologies, including these described withrespect to FIGS. 3A-3B, and/or 6B.

Various interleaving sequences may be employed when processing framesprovided by the cameras 552, 554, 556, 558. By way of illustration,encoding interleaved frame stream ABCDA . . . (e.g., the stream 620shown in FIG. 6B) comprising frames provided by the cameras 552, 554,556, 558 using a motion estimation encoder may provide motion due todisparity shown by arrows 562, 564, 566, 568 in FIG. 5B. Encoding theinterleaved frame stream ADCBA . . . , (e.g., 638 in FIG. 6B) using amotion estimation encoder, may provide motion due to disparity shown byarrows 569, 567, 565, 563 in FIG. 5B. Various other interleavingsequences may be utilized, such as, for example sequences 624, 628, 630,634 illustrated in, FIG. 6B may be utilized. Sequences comprisingtransitions between diagonally opposing cameras in FIG. 5B (e.g., AC,CA, BD, DB and/or other) may be used to, inter alia, determine disparityshown by arrows 544, 546.

FIG. 5C illustrates a linear multiple sensor element configurationuseful with determining multiple depths scales using image interleaving,according to one or more implementations. The configuration 570 maycomprise sensor elements 572, 574, 576, 578 disposed in a linear array.In one or more implementations, individual sensor elements may comprisecameras or camera sensors. Spacing between the elements 572, 574, 576,578 may be the same (uniform linear array) and/or varying (e.g., a powerlaw, random, and/or other). In some implementations, non-uniform spacingmay be used in order to implement e.g., a Vernier scale.

Various interleaving sequences may be employed when processing framesprovided by the elements 572, 574, 576, 758, such as, for examplesequences 620, 624, 628, 630, 634, 638 illustrated in, FIG. 6B and/orother sequences (e.g. ABBCCDDAA . . . ). Use of multiple elements of thearray 570 may enable determination of multiple disparity estimations,e.g., shown by arrows 580, 582, 584, 586, 588, 590. In someimplementations, the frames from individual sensor elements shown anddescribed above with respect to FIGS. 5A-5C (e.g., 510, 520, 530) may beacquired simultaneous with one another using, e.g., multiple camerasynchronization. The disparity estimations corresponding to differentsensor spacing (e.g., shown by arrows 580, 582, 584, 586, 588, 590 inFIG. 5C) may be characterized by different dynamic range, differentresolution, and/or precision, e.g., in accordance with Eqn. 1. By way ofan illustration, closely spaced sensing elements (e.g., 572, 574) may becapable of determining distance to objects disposed farther from thearray as compared to wide spaced elements (e.g., 572-578). Wide spacedelements (e.g., 572-578) may be capable of determining distance toobjects with greater precision (e.g., lower uncertainty) as compared toestimates produced by closely spaced sensing elements (e.g., 572, 574).

In some implementations, multiple elements (e.g., 572, 574, 576, 758)may be disposed in a non-linear array (e.g., rectangular and/or concave)thereby providing multiple perspectives and/or views of the scene to theprocessing component. Some views/perspectives may, e.g., reveal objectsthat may be hidden and/or partially obscured in other perspectives,thereby enabling more robust determination of object distance and/orobject detection. In some implementations, individual distance estimates(associated with individual camera pairs) may be combined using anyappropriate methodologies (e.g., averaging, thresholding, medianfiltering), and/or other techniques to obtain a resultant distanceestimate, characterized by greater precision and/or accuracy compared toindividual estimates. In one or more implementations, a distanceestimate associated with one camera pair may be selected as theresultant distance estimate, thereby enabling robust distancedetermination in presence of occlusions that may (at least partly) blockthe object in a given set of frames.

FIG. 7 depicts a motion extraction apparatus, according to one or moreimplementations. The apparatus 700 may comprise an encoder component 706configured to encode input video stream 702. The input 702 may compriseone or more frames received from an image sensor (e.g., charge-coupleddevice (CCD), CMOS device, and/or an active-pixel sensor (APS),photodiode arrays, and/or other image sensors). In one or moreimplementations, the input may comprise a pixel stream downloaded from afile. An example of such a file may include a stream of two-dimensionalmatrices of red green blue RGB values (e.g., refreshed at a 25 Hz orother suitable frame rate). It will be appreciated by those skilled inthe art when given this disclosure that the above-referenced imageparameters are merely exemplary, and many other image representations(e.g., bitmap, luminance-chrominance (YUV, YCbCr), cyan-magenta-yellowand key (CMYK), grayscale, and/or other image representations) areequally applicable to and useful with the various aspects of the presentdisclosure. Furthermore, data frames corresponding to other (non-visual)signal modalities such as sonograms, IR, radar or tomography images areequally compatible with the processing methodology of the disclosure, oryet other configurations.

The component 706 may comprise a specialized video encoder configured toimplement video encoding comprising a motion estimation operation. Inone or more implementations, the component 706 may comprise a dedicatedintegrated circuit (IC) disposed on a single or multiple die), acomponent of a processing system (e.g., video encoder block of aSnapdragon® system on a chip), an ASIC, an FPGA with a video encoderintellectual property (IP) core, an OEM printed circuit board, and/orother. Video encoding effectuated by the component 706 may comprise anyapplicable standard comprising motion estimation between current frameand preceding frame. Some encoding implementations may comprise MPEG-4,H.262, H.263, H.264, H.265 video encoder such as described above withrespect to FIG. 4A supra.

The component 706 may provide encoded video output 708. The output 708may be characterized by a lower data rate (e.g., as represented by fewerbits per frame) as compared to the input video signal 702. The output708 may comprise pixel luminance and/or chromaticity data. The output708 may comprise motion information, e.g., as illustrated in FIG. 9Awhich depicts output of a video encoder useful with the motionextraction methodology. In one or more implementations, the outputillustrated in FIG. 9A may correspond to occurrence of an object, e.g.,moving ball represented by a hashed circle 900 in FIG. 9A in input 702of FIG. 7. The encoded output 708 may comprise a luminance component(also referred to as “luma”) depicted by area 902 in FIG. 9A. Theencoded luminance may be comprised of a plurality of macroblocks 904.Size of the macroblock may be configured in accordance withspecifications of an application (e.g., encoding standard, video framesize, resolution, quality, refresh rate, bit depth, channel (e.g., luma,chroma), and/or other and be selected, for example, at 16×16 for lumachannel, 8×8 for chroma channel for H.264 encoder.

The encoded output 708 (that also may be referred to as the compressedvideo) may comprise motion information, denoted by area 910 in FIG. 9A.Motion information may comprise one or more vectors (e.g., 916)associated with one or more macroblock (e.g., 914).

Compressed video 708 in FIG. 7 may be provided to a processing component710. The component 710 may be configured to parse the compressed videostream 708 in order to obtain motion information (e.g., map of vectors916 in FIG. 9A). By way of an illustration, the motion information maycomprise a macroblock location L (e.g., index), x-component, andy-component of motion of pixels associated with the macroblock locationL. The extracted motion information 712 may be provided to anothercomponent. Various uses of the motion information may be contemplatedsuch as, for example, object detection by recognizing the shape of thesurface of the object, and/or by using depth to segment the scene,gesture detection by determining the orientation of the hands or otherbody parts, and/or other. In some implementations, the compressed videomay be provided via a pathway 714 to a target destination (e.g., generalpurpose processor for streaming to a display and/or other components).

FIG. 8 depicts a video processing system, comprising a differentialmotion extraction apparatus, according to one or more implementations.The system 800 of FIG. 8 may be configured to receive sensory input 802.In some implementations, the input 802 may comprise the input 702described above with respect to FIG. 7. The input 802 may be encoded bya video encoder component 806. In one or more implementations, thecomponent 806 may comprise the component 706 described above withrespect to FIG. 7. The component 806 may be configured to encode theinput 802 using one or more encoding formats (e.g., H.264). The encodedsignal 808 may be provided to component 810. In some implementations,the component 810 may be configured to parse the encoded signal 808 toextract motion information 812 by, e.g., extracting from the compressedvideo data the P slice (P-frame) data which contains the motioninformation (x and y components) or the macroblock motion for allmacroblocks covering the current frame. The extracted motion informationmay be used in controlling a robotic device.

The extracted motion information (e.g., 712, 812 in FIGS. 7-8,respectively) may comprise horizontal and/or vertical displacement(e.g., the motion vector components (dx, dy)) of a pixel group (e.g., amacroblock) between the current frame and a preceding frame. In someimplementations of video encoding useful with a pipeline-basedmultimedia framework (see, e.g., GStreamer framework,http://gstreamer.freedesktop.org/) the parsed motion information may berepresented using the YUV color model. In one such implementation, the(U,V) channels may represent the (dx,dy) displacement and the Y channelmay be used for representing additional information (e.g., indicates asto whether the current frame is the keyframe, macroblock size (e.g.,16×16, 8×8 and/or other size, and/or other information). Using the(Y,U,V) model to represent motion information may advantageously reducecomputational load on, e.g., the component 820, and enable access tomotion information without necessitating further decoding/encodingoperations in order to extract the motion vector components.

The input 802 may be processed by a processing component 820. Thecomponent 820 may comprise an artificial neuron network (ANN) comprisinga plurality of nodes. Individual nodes of the component 820 network maycomprise neuron units characterized by a receptive field, e.g., regionof space in which a presence of a stimulus may affect response of theneuron. In some implementations, the units may comprise spiking neuronsand the ANN may comprise a spiking neuron network, (SNN). Variousimplementations of SNNs may be utilized consistent with the disclosure,such as, for example, those described in co-owned, and co-pending U.S.patent application Ser. No. 13/774,934, entitled “APPARATUS AND METHODSFOR RATE-MODULATED PLASTICITY IN A SPIKING NEURON NETWORK” filed Feb.22, 2013, Ser. No. 13/763,005, entitled “SPIKING NETWORK APPARATUS ANDMETHOD WITH BIMODAL SPIKE-TIMING DEPENDENT PLASTICITY” filed Feb. 8,2013, Ser. No. 13/152,105, filed Jun. 2, 2011 and entitled “APPARATUSAND METHODS FOR TEMPORALLY PROXIMATE OBJECT RECOGNITION”, Ser. No.13/487,533, filed Jun. 4, 2012 and entitled “STOCHASTIC SPIKING NETWORKLEARNING APPARATUS AND METHODS”, Ser. No. 14/020,376, filed Sep. 9, 2013and entitled “APPARATUS AND METHODS FOR EVENT-BASED PLASTICITY INSPIKING NEURON NETWORKS”, Ser. No. 13/548,071, filed Jul. 12, 2012 andentitled “SPIKING NEURON NETWORK SENSORY PROCESSING APPARATUS ANDMETHODS”, commonly owned U.S. patent application Ser. No. 13/152,119,filed Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS ANDMETHODS”, Ser. No. 13/540,429, filed Jun. 29, 2012 and entitled “SENSORYPROCESSING APPARATUS AND METHODS”, Ser. No. 13/623,820, filed Sep. 20,2012 and entitled “APPARATUS AND METHODS FOR ENCODING OF SENSORY DATAUSING ARTIFICIAL SPIKING NEURONS”, Ser. No. 13/623,838, filed Sep. 20,2012 and entitled “SPIKING NEURON NETWORK APPARATUS AND METHODS FORENCODING OF SENSORY DATA”, Ser. No. 12/869,573, filed Aug. 26, 2010 andentitled “SYSTEMS AND METHODS FOR INVARIANT PULSE LATENCY CODING”, Ser.No. 12/869,583, filed Aug. 26, 2010, entitled “INVARIANT PULSE LATENCYCODING SYSTEMS AND METHODS”, Ser. No. 13/117,048, filed May 26, 2011 andentitled “APPARATUS AND METHODS FOR POLYCHRONOUS ENCODING ANDMULTIPLEXING IN NEURONAL PROSTHETIC DEVICES”, Ser. No. 13/152,084, filedJun. 2, 2011, entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANTOBJECT RECOGNITION”, Ser. No. 13/239,255 filed Sep. 21, 2011, entitled“APPARATUS AND METHODS FOR SYNAPTIC UPDATE IN A PULSE-CODED NETWORK”,Ser. No. 13/487,576 entitled “DYNAMICALLY RECONFIGURABLE STOCHASTICLEARNING APPARATUS AND METHODS”, filed Jun. 4, 2012, and U.S. Pat. No.8,315,305, entitled “SYSTEMS AND METHODS FOR INVARIANT PULSE LATENCYCODING” issued Nov. 20, 2012, each of the foregoing being incorporatedherein by reference in its entirety.

Receptive fields of the network 820 units may be configured to spanseveral pixels with the input 802 frames so as to effectuate sparsetransformation of the input 802. Various applicable methodologies may beutilized in order to effectuate the sparse transformation, including,for example, those described in co-pending and co-owned U.S. patentapplication Ser. No. 13/540,429, entitled “SENSORY PROCESSING APPARATUSAND METHODS”, filed Jul. 2, 2012, and U.S. patent application Ser. No.13/623,820, entitled “APPARATUS AND METHODS FOR ENCODING OF SENSORY DATAUSING ARTIFICIAL SPIKING NEURONS”, filed on Sep. 20, 2012, each of theforegoing being incorporated herein by reference in its entirety. Insome implementations, the encoding may comprise a sparse transformation,described in, e.g., U.S. patent application Ser. No. 14/191,383,entitled “APPARATUS AND METHODS FOR TEMPORAL PROXIMITY DETECTION”, filedon Feb. 26, 2014, the foregoing being incorporated herein by referencein its entirety.

The output 812 of the encoder 820 may be provided to the processingcomponent 820. In some implementations, the component 820 may use themotion information 812 in order to determine characteristics (e.g.,location, dimension, shape, and/or other) of one or more objects insensory input 802. In one or more implementations, the component 820 maycomprise an adaptive predictor component configured to determine acontrol output 826 for a robotic device (e.g., the vehicle 100, 160 inFIGS. 1A-1B) based on the input 812 and/or inputs 802, 812. In someimplementations of autonomous vehicle navigation, the input 812 and/or802 may be used by the component 820 in order to predict control signalconfigured to cause the vehicle 160 in FIG. 1B to execute an obstacleavoidance action. Various implementations of predictors may be employedwith the motion detection approach described herein, including, e.g.,U.S. patent application Ser. No. 13/842,530, entitled “ADAPTIVEPREDICTOR APPARATUS AND METHODS”, filed on Mar. 15, 2013, the foregoingbeing incorporated herein by reference in its entirety.

FIG. 9B illustrates motion of an object obtained from encoded video,according to one or more implementations. Hashed area 922 in FIG. 9B mayrepresent luminance component of an image of a ball (e.g., 900 in FIG.9A). The encoded output of FIG. 9A may comprise motion information,denoted by area 920 in. Motion information may comprise one or morevectors (e.g., 926) associated with one or more macroblock (e.g., 924).Encoded representations of FIGS. 9A-9B may be used to determine temporaldistribution of motion associated with the ball 900. Motion patterncomprising alternating opposing motion vectors 916, 926 may be employedto communicate an action indication to a robotic device. In someimplementations, a user may shake an object from left to right in frontof a camera of an autonomous vehicle in order to indicate a target to befollowed.

FIG. 9C illustrates spatial distribution of motion extracted fromencoded video, according to one or more implementations. Therepresentation shown in FIG. 9C may comprise portion 930 comprising afirst plurality of macroblocks 932 characterized by first motiondirection 936. The representation shown in FIG. 9C may comprise portion940 comprising a second plurality of macroblocks 942 characterized bysecond motion direction 946. The spatial motion map illustrated in FIG.9C may be employed to communicate an action indication to a roboticdevice. In some implementations, a user wave arms (in a crisscrossmanner) in order to indicate to a robotic device a stop, and/or othercommand.

In some implementations (not shown) motion information for a given framemay be characterized by a plurality of different motion vectors due to,e.g., motion of different objects, camera pan/zoom operation, and/orvideo acquisition from a moving platform. By way of an illustration ofoperation of the robotic vehicle 160 of FIG. 1B, video signal obtainedby the camera 166 may comprise a representation of human making gesturessuperimposed on a moving background. Detection of one motion associatedwith the gestures on a background motion may be referred to asdifferential motion detection. In some implementations, the backgroundmay be characterized by spatially coherent (uniform) motion. Backgroundmotion for a given frame may be estimated and removed. The resultantmotion field may be analyzed in order to determine, e.g., handgesture(s) and/or objects. In one or more implementations, a sequence offrames may be characterized by the background motion that is temporallycoherent over timescale associated with the frame sequence. Backgroundmotion for the sequence of frames may be estimated and removed fromindividual frames within the sequence. The resultant motion field may beanalyzed in order to determine, e.g., hand gesture(s) and/or objects.

FIGS. 10-15 illustrate methods 1000, 1100, 1200, 1300, 1400, 1500 fordetermining and using motion information from encoded video. Theoperations of methods 1000, 1100, 1200, 1300, 1400, 1500 presented beloware intended to be illustrative. In some implementations, method 1000,1100, 1200, 1300, 1400, 1500 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofmethod 1000, 1100, 1200, 1300, 1400, 1500 are illustrated in FIGS. 10-15and described below is not intended to be limiting.

In some implementations, methods 1000, 1100, 1200, 1300, 1400, 1500 maybe implemented in one or more processing devices (e.g., a digitalprocessor, an analog processor, a digital circuit designed to processinformation, an analog circuit designed to process information, a statemachine, and/or other mechanisms for electronically processinginformation). The one or more processing devices may include one or moredevices executing some or all of the operations of methods 1000, 1100,1200, 1300, 1400, 1500 in response to instructions stored electronicallyon an electronic storage medium. The one or more processing devices mayinclude one or more devices configured through hardware, firmware,and/or software to be specifically designed for execution of one or moreof the operations of methods 1000, 1100, 1200, 1300, 1400, 1500.

FIG. 10 illustrates a method of determining a salient feature usingencoded video motion information, in accordance with one or moreimplementations.

Operations of method 1000 may be applied to processing of sensory data(e.g., audio, video, RADAR imagery, SONAR imagery, and/or otherimagery), observation data, motor command activity in a robotic system,and/or other systems or data.

At operation 1002 of method 1000, one or more a consecutive input videoframes may be encoded. In one or more implementations, the frames may beprovided by an image sensor (e.g., CCD, CMOS device, and/or APS,photodiode arrays, and/or other image sensors). In some implementations,the input may comprise a pixel stream downloaded from a file, such as astream of two-dimensional matrices of red green blue RGB values (e.g.,refreshed at a 25 Hz or other suitable frame rate). It will beappreciated by those skilled in the art when given this disclosure thatthe above-referenced image parameters are merely exemplary, and manyother image representations (e.g., bitmap, luminance-chrominance YUV,YCbCr, CMYK, grayscale, and/other image representations) may beapplicable to and useful with the various implementations. Data framescorresponding to other (non-visual) signal modalities such as sonograms,IR, radar or tomography images may be compatible with the processingmethodology of the disclosure, and/or other configurations. The framesmay form real-time (live) video. In one or more implementations, theencoding may comprise operations performed in accordance with anyapplicable encoding standard comprising motion estimation betweensuccessive frames (e.g., H.263, H.264, and/or other).

At operation 1004 encoded video may be parsed in order to obtain motioninformation. In some implementations, the motion information maycomprise a plurality of motion vectors and their locations as associatedwith one or more macroblocks within the encoded frame (e.g., the vector916 of macroblock 914 in FIG. 9A).

At operation 1006 a salient feature may be determined using motioninformation. In one or more implementations, the feature determinationmay be based on analysis of motion spatial map within a given frame(e.g., the motion map comprising the area 930, 940 in FIG. 9C). In oneor more implementations, the feature determination may be configuredbased on analysis of motion temporal characteristics (e.g., persistenceof motion features in a given location over multiple frames, comparingmotion at a given location between two or more frames, and/or other).

FIG. 11 illustrates a method of data processing useful for determiningfeatures, in accordance with one or more implementations.

At operation 1102 live video may be obtained during execution of a task.In some implementations of robotic vehicle navigation, the video may beobtained with a video camera disposed on the vehicle. The video streammay be encoded using any applicable standard comprising motionestimation operation (e.g., H.263, H.264, and/or other).

At operation 1104 motion information may be determined from the encodedvideo stream. In some implementations, the encoded video stream may beparsed in order to obtain motion information. In some implementations,the motion information may comprise a plurality of motion vectors andtheir locations as associated with one or more macroblocks within theencoded frame (e.g., the vector 916 of macroblock 914 in FIG. 9A).

At operation 1106 a location of an object within video frame may bedetermined using motion information obtained at operation 1104. In oneor more implementations, the location determination may be based ontemporal and/or spatial persistence (coherence) of motion over a givenarea and/or over several frames. By way of an illustration, occurrenceof a plurality of macroblocks characterized by motion vectors within agiven margin from one another (e.g., 5-20% in one implementation) in agiven frame may indicate a moving object.

At operation 1108 the object associated with the location determined atoperation 806 may be related to a task action. Based on the actiondetermination, a control signal may be provided. In someimplementations, the control signal provision may be configured based onoperation of an adaptive predictor, e.g., such as described in U.S.patent application Ser. No. 13/842,530, entitled “ADAPTIVE PREDICTORAPPARATUS AND METHODS”, filed on Mar. 15, 2013, incorporated supra.

At operation 1110, the action may be executed. By way of anillustration, the object may comprise a ball 174 in FIG. 1A, the motioninformation may indicate the ball moving to the left of the vehicle, thetask may comprise target pursuit, and the action may comprise a leftturn by the vehicle.

FIG. 12 is a logical flow diagram illustrating a method of executing anaction configured based on a gesture detected using motion information,in accordance with one or more implementations.

At operation 1202 motion information may be determined using one or moreencoded video frames. In some implementations, the motion informationmay comprise motion vectors due to gestures of a human (e.g., vectors936, 946 in FIG. 9B).

At operation 1204 a spatio-temporal distribution of the motioninformation may be determined. In some implementations of spatial motiondistribution, the motion map may comprise more areas of macroblocks(e.g., the area 910 in FIG. 9A and/or 90 in FIG. 9C) characterized bysimilar motion vector components. (e.g., components of vector 946 inFIG. 6C). In some implementations, temporal motion distribution may bedetermined by analyzing motion associated with a portion of the frame(e.g., the area 940 in FIG. 6C) over a plurality of consecutive frames.

At operation 1206 a gesture may be determined based on a spatio-temporalpattern within the motion distribution. By way of an illustration, apattern of alternating motion vectors of a rectangular area within theframe may correspond to a crisscross motion of arms by the userindicating an alert (e.g., a stop) command to the robotic device. Insome implementations, motion information for a given frame may becharacterized by a plurality of different motion vectors due to, e.g.,motion of different objects, camera pan/zoom operation, and/or videoacquisition from a moving platform. By way of an illustration ofoperation of the robotic vehicle 160 of FIG. 1B, video signal obtainedby the camera 166 may comprise a representation of human making gesturessuperimposed on a moving background.

At operation 1208, an action may be executed in accordance with thegesture determined at operation 1206. For example, upon detecting thecrisscross arm motion the robotic device may stop trajectory navigation.

The motion-based gesture detection methodology described herein may beemployed for operation of a robotic appliance and/or remotely operateddevice. In some implementations, gesture detection may be effectuated bya spoofing controller, e.g., such as described in U.S. patentapplication Ser. No. 14/244,892, entitled “ADAPTIVE PREDICTOR APPARATUSAND METHODS”, filed on Apr. 3, 2014, incorporated herein by reference inits entirety. The spoofing controller may be trained to developassociations between the detected gestures and one or more remotecontrol commands (by e.g., an IR remote operating a home appliance(TV)). The developed associations may enable the spoofing controller tooperate the TV in accordance with gestured of a user in lieu of theremote controller commands.

A commercially available off-the shelf hardware video encoder (e.g.,1006 in FIG. 10) may be used to provide a compressed video stream.Typically, hardware encoders may be utilized in order to reduce videodata rate in order to reduce storage, and/or bandwidth load associatedwith manipulation of video information. Motion extraction methodologydescribed herein may advantageously enable determination of motioninformation by an application device using available compressed videoalbeit that is used for other purposes (e.g., reduction in storageand/or bandwidth). Use of available compressed video, comprising motionestimation data (e.g., MPEG-4) may substantially reduce computationalload associated with motion determination, compared to existingtechniques such as optic flow, and/or motion estimation algorithms suchas but not limited to: block-matching algorithm, phase correlation, aswell as determining locations of features and estimating the motion ofthose features.

FIG. 13 illustrates a method of determining a depth of visual sceneusing encoded interleaved stereo image information, in accordance withone or more implementations.

At operation 1302 of method 1300, a monocular frame configuration may beobtained using a stereo image of a visual scene. In someimplementations, the monocular frame configuration may comprise aninterleaved frame sequence 324, 342 described above with respect toFIGS. 3A-3B. In one or more implementations of multi-camera imageacquisition, the monocular frame configuration may comprise aninterleaved frame sequence such as shown in FIGS. 6A and/or 6B.

At operation 1304 monocular frame sequence may be encoded using a motionestimation encoder. In some implementations, the encoding may beperformed by a specialized video encoder comprising a motion estimationoperation (e.g., MPEG-4, H.264, or other).

At operation 1306 depth of visual scene may be determined using motioninformation of the encoded data obtained at operation 1304. In one ormore implementations, the motion information may be obtained by toparsing the compressed video stream (e.g., 422 in FIG. 4A). By way of anillustration, the motion information may comprise a macroblock locationL (e.g., index), x-component, and y-component of motion of pixelsassociated with the macroblock location L. The extracted motioninformation may be used for disparity and or distance determination.Various uses of the depth information may be contemplated such as, forexample, object detection, object localization, distance estimation,trajectory planning, gesture detection, and/or other.

FIG. 14 illustrates a method of determining distance to objects usingmotion of interleaved image stream, in accordance with one or moreimplementations.

At operation 1402 of method 1400, frames from multiple cameras may becombined into an interleaved frame stream. In one or moreimplementations the interleaved frame stream may comprise a framesequence such as shown in FIGS. 6A and/or 6B.

At operation 1404 the interleaved frame sequence may be encoded using amotion estimation encoder. In some implementations, the encoding may beperformed by a specialized video encoder comprising a motion estimationoperation (e.g., MPEG-4, H.264, or other).

At operation 1406 an object may be detected based on a spatio-temporalpattern within the motion distribution of the encoded data. In one ormore implementations, the motion information may be obtained by toparsing the compressed video stream (e.g., 422 in FIG. 4A comprising,e.g., encoded frames 356, 360, 364 shown and described with respect toFIG. 3B). Object detection may be effectuated using any applicablemethodologies including these described above with respect to FIGS.9A-9C.

At operation 1408, distance to the object identified at operation 1406may be determined. The distance determination may be configured based onthe disparity data that may be obtained from the motion information ofthe encoded data (e.g., the frames 354, 358, 362 in FIG. 3B). Varioususes of the distance information may be contemplated such as, forexample, object detection, trajectory planning, gesture detection,obstacle avoidance, and/or other.

FIG. 15 illustrates a method of executing an action configured based ondetecting an object in motion information, in accordance with one ormore implementations.

At operation 1502 of method 1500 interleaved frame stream may be encodedusing a motion estimation encoder. In some implementations, the encodingmay be performed by a specialized video encoder comprising a motionestimation operation (e.g., MPEG-4, H.264, or other). into encoded datausing encoder with motion estimation

At operation 1504 distance to the object may be determined usingdisparity determined from the motion information of the encoded data.The distance determination may be configured based on the disparity datathat may be obtained from the motion information of the encoded data(e.g., the frames 354, 358, 362 in FIG. 3B).

At operation 1506 an action may be associated with the object parametersdetermined at operation 1504. In some implementations, the objectparameters may comprise object features (e.g., shape, color, identity),location, distance, speed, and/or other. By way of an illustration, theobject may comprise a ball 112 in FIG. 1A rolling across the path of thevehicle 100. The distance to the ball 112 and the ball motion data mayindicate that the vehicle 100 may collide with the ball 112. The actionmay comprise a turn left/right and/or reducing the speed of the vehicle100.

At operation 1510 the action may be executed. Action execution may beconfigured based on output of an adaptive predictor apparatus configuredto predict control signal for the robotic vehicle 100. In someimplementations, the predictor may be operated in accordance with alearning process such as described, for example, in U.S. patentapplication Ser. No. 13/842,530, entitled “ADAPTIVE PREDICTOR APPARATUSAND METHODS”, filed on Mar. 15, 2013, the foregoing being incorporatedsupra.

FIGS. 16A-16D illustrate gestures of a human operator used forcommunicating control indications to a robotic device comprisingdistance determination apparatus described herein, in accordance withone or more implementations.

FIG. 16A is a top view of a user and may illustrate a base posture ofthe user. FIG. 16B may depict user gestures 1600 communicating a rightturn action to a robotic device (e.g., the vehicle 100 in FIG. 1A. Therobotic device 100 may utilize stereo images provided by the cameras106, 108 in order to detect position of the user arms 1608, 1608. Insome implementations, the arm 1608, 1604 position may be determinedusing the distance determination methodology configured based onencoding interleaved left/right portions of the stereo imagery. By wayof an illustration, the gesture in FIG. 16B may be determining based ona comparison of distance between the robot and the user arms inpositions 1604, 1608 in FIG. 16B relative the user arms in position 1624in FIG. 16A. In one or more implementations, the gesture in FIG. 16B maybe determining based on a comparison of distance between the robot andthe user arms in positions 1604, 1608 relative the user head 1602 inFIG. 16B.

FIG. 16C is a side view of the user and may depict user gesture 1610communicating a stop action to a robotic device (e.g., the vehicle 100in FIG. 1A). The robotic device 100 may utilize stereo images providedby the cameras 106, 108 in order to detect position of the user arms,head 1642, 1612, and/or hands 1614, 1644. In some implementations, thehand 1642, 1644 position may be determined using the distancedetermination methodology configured based on encoding interleavedleft/right portions of the stereo imagery. By way of an illustration,the gesture in FIG. 16C may be obtained based on a comparison ofdistance between the robot and the user hands in position 1614 in FIG.16C relative the user hand in position 1644 in FIG. 16D. In one or moreimplementations, the gesture in FIG. 16C may be determined based on acomparison of distance between the robot and the user hand in position1614 relative the user head 1612 in FIG. 16C. In some implementations(not shown) the user may communicate an indication to the robotic deviceby, e.g., appearing in view of the camera. By way of an illustrating,the user stepping in front of the vehicle may indicated to the vehicle astop action

The present disclosure also contemplates a computerized controllerapparatus for implementing, inter alia, motion and/or distancedetermination methodology in accordance with one or moreimplementations.

The controller apparatus (not shown) may comprise a processing moduleconfigured to receive sensory input from sensory block (e.g., cameras106, 108 in FIG. 1A). In some implementations, the sensory module maycomprise audio input/output portion. The processing module may beconfigured to implement signal processing functionality (e.g., distanceestimation, object detection based on motion maps, and/or other).

The controller apparatus may comprise memory configured to storeexecutable instructions (e.g., operating system and/or application code,raw and/or processed data such as raw image fames and/or object views,teaching input, information related to one or more detected objects,and/or other information).

In some implementations, the processing module may interface with one ormore of the mechanical, sensory, electrical, power components,communications interface, and/or other components via driver interfaces,software abstraction layers, and/or other interfacing techniques. Thus,additional processing and memory capacity may be used to support theseprocesses. However, it will be appreciated that these components may befully controlled by the processing module. The memory and processingcapacity may aid in processing code management for the controllerapparatus (e.g. loading, replacement, initial startup and/or otheroperations). Consistent with the present disclosure, the variouscomponents of the device may be remotely disposed from one another,and/or aggregated. For example, the instructions operating the hapticlearning process may be executed on a server apparatus that may controlthe mechanical components via network or radio connection. In someimplementations, multiple mechanical, sensory, electrical units, and/orother components may be controlled by a single robotic controller vianetwork/radio connectivity.

The mechanical components of the controller apparatus may includevirtually any type of device capable of motion and/or performance of adesired function or task. Examples of such devices may include one ormore of motors, servos, pumps, hydraulics, pneumatics, stepper motors,rotational plates, micro-electro-mechanical devices (MEMS),electroactive polymers, shape memory alloy (SMA) activation, and/orother devices. The sensor devices may interface with the processingmodule, and/or enable physical interaction and/or manipulation of thedevice.

The sensory devices may enable the controller apparatus to acceptstimulus from external entities. Examples of such external entities mayinclude one or more of video, audio, haptic, capacitive, radio,vibrational, ultrasonic, infrared, motion, and temperature sensorsradar, lidar and/or sonar, and/or other external entities. The modulemay implement logic configured to process user commands (e.g., gestures)and/or provide responses and/or acknowledgment to the user.

The electrical components may include virtually any electrical devicefor interaction and manipulation of the outside world. Examples of suchelectrical devices may include one or more of light/radiation generatingdevices (e.g. LEDs, IR sources, light bulbs, and/or other devices),audio devices, monitors/displays, switches, heaters, coolers, ultrasoundtransducers, lasers, and/or other electrical devices. These devices mayenable a wide array of applications for the apparatus in industrial,hobbyist, building management, medical device, military/intelligence,and/or other fields.

The communications interface may include one or more connections toexternal computerized devices to allow for, inter alia, management ofthe controller apparatus. The connections may include one or more of thewireless or wireline interfaces discussed above, and may includecustomized or proprietary connections for specific applications. Thecommunications interface may be configured to receive sensory input froman external camera, a user interface (e.g., a headset microphone, abutton, a touchpad, and/or other user interface), and/or provide sensoryoutput (e.g., voice commands to a headset, visual feedback, and/or othersensory output).

The power system may be tailored to the needs of the application of thedevice. For example, for a small hobbyist robot or aid device, awireless power solution (e.g. battery, solar cell, inductive(contactless) power source, rectification, and/or other wireless powersolution) may be appropriate. However, for building managementapplications, battery backup/direct wall power may be superior, in someimplementations. In addition, in some implementations, the power systemmay be adaptable with respect to the training of the apparatus 1800.Thus, the controller apparatus may improve its efficiency (to includepower consumption efficiency) through learned management techniquesspecifically tailored to the tasks performed by the controllerapparatus.

Various aspects of the disclosure may advantageously be applied todesign and operation of apparatus configured to process sensory data.Implementations of the principles of the disclosure may be applicable todetecting objects by a wide variety of stationary and portable videodevices, such as, for example, smart phones, portable communicationdevices, notebook, netbook and tablet computers, surveillance camerasystems, and practically any other computerized device configured toprocess vision data. The motion information may be used as a proxy foroptic flow (estimated motion (dx,dy) on a grid across the frame of thevideo). Use of available hardware encoders to obtain motion data mayreduce energy use by portable devices, enable motion detection on higherresolution video (e.g., resolutions greater than 320×240), improvemotion detection resolution in order to, e.g., detect gestures, comparedto optic flow detection techniques.

Interleaving of frames from multiple spatially displaced cameras mayenable determination of binocular disparity between pairs of cameraimages using motion estimation. Use of an off-the shelf commerciallyavailable hardware video encoder (e.g., MPEG-4, H.265 and/or otherencoder) comprising motion estimation, may substantially reduce cost,size, energy use of a motion estimation component, compared to use ofoptical flow for determining motion. Encoded into video may be parsed toobtain motion information. Motion corresponding to a pair of frames fromdisplaced cameras may be interpreted as a measure of disparity. Thedisparity may be utilized in order to determine depth of visual sceneand/or distance to objects within visual scene. By way of anillustration, embodying a motion determination component and/or adistance determination component of the disclosure in a robotic vehicle(e.g., 100, 1700 in FIGS. 1A, 17) may extend duration of autonomousoperation of the robotic apparatus due to, in part, lower energy usethat may be associated with motion/distance detection based on videoencoded using hardware encoder, as compared to using video processing ina CPU (e.g., optical flow, and/or pixel block matching). The increasedautonomy may be characterized by the robotic device capability toperform a given action (e.g., a flight route and/or surveillance route)an additional number of times without recharging, and/or being capableof completing longer routes on a given charge as compared to the priorart solutions. In one or more implementations, the reduced energy usemay be leveraged for producing a smaller, lighter and/or less costlyrobot that may be capable of performing the action (e.g., navigate agiven route) compared to the comparable device of the prior art.

An autonomous robotic device comprising a hardware video encoder may becapable to perform motion estimation for obstacle avoidance, trackingmoving objects, stabilization, platform and/or enabling the robot tolearn its own self motion. By way of an illustration, a robotic devicemay be configured to follow a target (e.g., a person, a ball 112 in FIG.1A, and/other object) at a distance (e.g., 110 in FIG. 1A). In one ormore implementations of tracking, the robotic device may be configuredto maintain distance from target within a range (e.g., not to exceed 50meters, and not to approach closer than 2 meters when following a forvehicle, and/or not to exceed 5 meters, and not to approach closer than0.25 meters when following the ball 112 in FIG. 1A. In one or moreimplementations of object tracking, approach, avoid, and/or other,controller of the robotic device may be configured to determine distanceto the target and motion of the target using, e.g., the alternatinginterleaving methodology shown and described with respect to FIG. 3B. Insome implementations, the distance may be determined using theinterleaving methodology; the motion may be determined using the videoencoding methodology.

FIG. 17 illustrates use of distance determination methodology by anunmanned robotic apparatus configured for autonomous navigation, inaccordance with one or more implementations. The unmanned autonomousvehicle (AUV) 1700 may comprise a plurality of cameras 1702 disposedspatially from one another. Video stream provided by the cameras 1702may be interleaved and encoded using any applicable methodologydescribed herein (e.g., with respect to FIGS. 3A-3B, 6A-6B, and/or9A-9C). The encoding may enable controller of the vehicle 1700 (e.g.,400, 440 in FIGS. 4A-4B, and/or 700, 800 FIGS. 7-8) do determinedistance 1706 between the vehicle 1700 and the landing location 1712,and/or distance 1718 to obstacles (e.g., 1710). The controller mayutilize the distance and/or vehicle motion information to controlactuators 1704 when landing, during take-off and or navigating aroundobstacles.

In some, implementations, the motion detection methodology describedherein may be employed for detecting salient objects in video input. Thesaliency of an item (such as an object, a person, a pixel, and/or other)may be described by a characteristic by which the item may stand outrelative to its neighbors. For example, a salient vehicle may comprise avehicle that may be moving differently (e.g., going slower/faster thanthe rest of the traffic, weaving from lane to lane) compared to the restof the traffic. A salient object for target approach may comprise astationary and/or moving ball on a moving background due to self-motionby the vehicle.

Implementations of the principles of the disclosure may be furtherapplicable to a wide assortment of applications including computer-humaninteraction (e.g., recognition of gestures, voice, posture, face, and/orother interactions), controlling processes (e.g., processes associatedwith an industrial robot, autonomous and other vehicles, and/or otherprocesses), augmented reality applications, access control (e.g.,opening a door based on a gesture, opening an access way based ondetection of an authorized person), detecting events (e.g., for visualsurveillance or people or animal counting, tracking).

A video processing system of the disclosure may be implemented in avariety of ways such as, for example, a software library, an IP coreconfigured for implementation in a programmable logic device (e.g.,FPGA), an ASIC, a remote server, comprising a computer readableapparatus storing computer executable instructions configured to performfeature detection. Myriad other applications exist that will berecognized by those of ordinary skill given the present disclosure.

Although the system(s) and/or method(s) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation can be combined with one or morefeatures of any other implementation.

EXHIBIT I—EXEMPLARY COMPUTER CODE © Copyright 2014 Brain Corporation,All rights reserved void ff_h263_update_motion_val(MpegEncContext * s){ const int mb_xy = s->mb_y * s->mb_stride + s->mb_x;     //FIXME a lotof that is only needed for !low_delay  const int wrap = s->b8_stride; const int xy = s->block_index[0];  int motion_x=0, motion_y=0;  constint block_size= 8>>s->avctx->lowres; s->current_picture.mbskip_table[mb_xy]= s->mb_skipped;  if(s->mv_type!= MV_TYPE_8X8){   if (s->mb_intra) {    motion_x = 0;    motion_y = 0;  } else if (s->mv_type == MV_TYPE_16X16) {    motion_x =s->mv[0][0][0];    motion_y = s->mv[0][0][1];   } else /*if (s->mv_type== MV_TYPE_FIELD)*/ {    int i;    motion_x = s->mv[0][0][0] +s->mv[0][1][0];    motion_y = s->mv[0][0][1] + s->mv[0][1][1];   motion_x = (motion_x>>1) | (motion_x&1);    for(i=0; i<2; i++){    s->p_field_mv_table[i][0][mb_xy][0]= s->mv[0][i][0];    s->p_field_mv_table[i][0][mb_xy][1]= s->mv[0][i][1];    }   s->current_picture.ref_index[0][4*mb_xy ]=   s->current_picture.ref_index[0][4*mb_xy + 1]= s->field_select[0][0];   s->current_picture.ref_index[0][4*mb_xy + 2]=   s->current_picture.ref_index[0][4*mb_xy + 3]= s->field_select[0][1];  }   /* no update if 8X8 because it has been done during parsing */  s->current_picture.motion_val[0][xy][0] = motion_x;  s->current_picture.motion_val[0][xy][1] = motion_y;  s->current_picture.motion_val[0][xy + 1][0] = motion_x;  s->current_picture.motion_val[0][xy + 1][1] = motion_y;  s->current_picture.motion_val[0][xy + wrap][0] = motion_x;  s->current_picture.motion_val[0][xy + wrap][1] = motion_y;  s->current_picture.motion_val[0][xy + 1 + wrap][0] = motion_x;  s->current_picture.motion_val[0][xy + 1 + wrap][1] = motion_y;  if(s->avctx->debug_mv) {    for (int i=0;i<2*block_size;i++)memset(s->dest[0] + i * s->linesize, 120 +s- >current_picture.key_frame * 5, 2*block_size);    for (inti=0;i<block_size;i++) memset(s->dest[1] + i * s->uvlinesize, 128 +motion_x, block_size);    for (int i=0;i<block_size;i++)memset(s->dest[2] + i * s->uvlinesize, 128 + motion_y, block_size);   } } else {   if(s->avctx->debug_mv) {    for (int i=0;i<block_size*2;i++)memset(s->dest[0] + i * s->uvlinesize, 130 + block_size*2);    for (intywrap=0, y=0;y<2;ywrap+=wrap,y++) {     for (int x=0;x<2;x++) {     motion_x = s->current_picture.motion_val[0][xy + x + ywrap][0];     motion_y = s->current_picture.motion_val[0][xy + x + ywrap][1];     for (int i=0;i<block_size/2;i++) memset(s->dest[1] +x*block_size/2 + (i + y*block_size/2) * s->uvlinesize, 128 + motion_x,block_size/2);      for (int i=0;i<block_size/2;i++) memset(s->dest[2] +x*block_size/2 + (i + y*block_size/2) * s->uvlinesize, 128 + motion_y,block_size/2);     }    }   }  }  if(s->encoding){ //FIXME encoding MUSTbe cleaned up   if (s->mv_type == MV_TYPE_8X8)   s->current_picture.mb_type[mb_xy]= MB_TYPE_L0 | MB_TYPE_8x8;   elseif(s->mb_intra)    s->current_picture.mb_type[mb_xy]= MB_TYPE_INTRA;  else    s->current_picture.mb_type[mb_xy]= MB_TYPE_L0 | MB_TYPE_16x16; } }

What is claimed:
 1. A system for instructing a robot to execute a physical task, comprising: at least one processor; and a non-transitory computer readable media having computer executable instructions stored therein, which, when executed by the at least processor configure the at least one processor to, receive a plurality of images associated with a plurality of spatially separated cameras, at least a portion of the plurality of images depicting an object, determine, from the at least the portion of the plurality of images, a pattern of movement associated with the object based on disparity information derived from two or more of the plurality of images, the pattern of movement within the plurality of images is partitioned into a plurality of clusters wherein a largest area cluster is associated with a prevailing motion and is removed from the frame to obtain residual motion distribution associated with the object, the prevailing motion corresponding to apparent motion caused by at least one of a moving background or motions of the robot, generate, based on the pattern of movement, a command signal configured to instruct the robot to execute a physical action, and send the command signal to the robot, wherein the non-transitory computer readable media comprises an artificial neural network configured to, receive as input the plurality of images and the pattern of movement associated with the object to produce the command signal.
 2. The system of claim 1, wherein the robot is configured to execute the physical action responsive to receiving the command signal.
 3. The system of claim 1, wherein the plurality of images comprise a monocular frame sequence comprising an interleaved frame sequence derived from frame sequences obtained from the plurality of spatially separated cameras.
 4. The system of claim 3, further comprising: a motion estimation encoder, wherein the monocular frame sequence is encoded in part using the motion estimation encoder.
 5. The system of claim 1, wherein the artificial neural network generates a sparse transformation of the one or more images.
 6. The system of claim 1, wherein the plurality of encoded images are generated by encoding an interleaved sequence of images from three or more cameras of the plurality of spatially separate cameras.
 7. The system of claim 6, wherein the interleaved sequence of images comprises a transition between diagonally opposing cameras.
 8. A non-transitory computer readable media having computer executable instructions stored therein, which, when executed by the at least processor coupled to a robot configure the at least one processor to, receive a plurality of images associated with a plurality of spatially separated cameras, at least a portion of the plurality of images depicting an object; determine, from the at least the portion of the plurality of images, a pattern of movement associated with the object based on disparity information derived from two or more of the plurality of images, the pattern of movement within the plurality of images is partitioned into a plurality of clusters wherein a largest area cluster is associated with a prevailing motion and is removed from the frame to obtain residual motion distribution associated with the object, the prevailing motion corresponding to apparent motion caused by at least one of a moving background or motions of the robot; generate, based on the pattern of movement, a command signal configured to instruct the robot to execute a physical action; and send the command signal to the robot, wherein the non-transitory computer readable media comprises an artificial neural network configured to, receive as input the plurality of images and the pattern of movement associated with the object to produce the command signal.
 9. The non-transitory computer readable media of claim 8, wherein the robot is configured to execute the physical action responsive to receiving the command signal.
 10. The non-transitory computer readable media of claim 8, wherein the plurality of images comprise a monocular frame sequence comprising an interleaved frame sequence derived from frame sequences obtained from the plurality of spatially separated cameras.
 11. The non-transitory computer readable media of claim 10, wherein the monocular frame sequence is encoded in part using a motion estimation encoder.
 12. The non-transitory computer readable media of claim 8, wherein the artificial neural network generates produces a sparse transformation of the one or more images.
 13. The non-transitory computer readable media of claim 8, wherein the plurality of encoded images are generated by encoding an interleaved sequence of images from three or more cameras of the plurality of spatially separate cameras.
 14. The non-transitory computer readable media of claim 13, wherein the interleaved sequence of images comprises a transition between diagonally opposing cameras.
 15. A method for instructing a robot to execute a physical task, comprising: receiving as input a plurality of images associated with a plurality of spatially separated cameras, at least a portion of the plurality of images depicting an object; generating a sparse transformation of one or more of the plurality of images; determining, from the at least the portion of the plurality of images, a pattern of movement associated with the object based on disparity information derived from two or more of the plurality of images wherein the pattern of movement associated with the object is determined by partitioning the plurality of images into a plurality of clusters wherein a largest area cluster is associated with a prevailing motion, removing the largest area cluster from the frame and obtaining residual motion distribution associated with the object, the prevailing motion corresponding to apparent motion caused by at least one of a moving background or motions of the robot; generating, based on the pattern of movement, a command signal configured to instruct the robot to execute a physical action; and sending the command signal to the robot; wherein the non-transitory computer readable media comprises an artificial neural network configured to, receive as input the plurality of images and the pattern of movement associated with the object to produce the command signal.
 16. The method of claim 15, wherein the robot is configured to execute the physical action responsive to receiving the command signal.
 17. The method of claim 15, wherein the plurality of images comprise a monocular frame sequence comprising an interleaved frame sequence derived from frame sequences obtained from the plurality of spatially separated cameras.
 18. The method of claim 17, wherein the monocular frame sequence is encoded in part using a motion estimation encoder.
 19. The method of claim 15, wherein the artificial neural network generates produces a sparse transformation of the one or more images.
 20. The method of claim 15, wherein, the plurality of encoded images are generated by encoding an interleaved sequence of images from three or more cameras of the plurality of spatially separate cameras, and the interleaved sequence of images comprises a transition between diagonally opposing cameras. 